[Reformer] remove reformer pad_token_id (#7991)

* remove reformer pad_token_id

* fix pegasus

src/transformers/tokenization_pegasus.py

@@ -47,8 +47,8 @@ class PegasusTokenizer(ReformerTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    def __init__(self, *args, pad_token="<pad>", **kwargs):
+        super().__init__(*args, **kwargs, pad_token="<pad>")
         # Don't use reserved words added_token_encoder, added_tokens_decoder because of
         # AssertionError: Non-consecutive added token '1' found. in from_pretrained
         assert len(self.added_tokens_decoder) == 0

src/transformers/tokenization_reformer.py

@@ -86,19 +86,10 @@ class ReformerTokenizer(PreTrainedTokenizer):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     model_input_names = ["attention_mask"]
 
-    def __init__(
-        self,
-        vocab_file,
-        eos_token="</s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        additional_special_tokens=[],
-        **kwargs
-    ):
+    def __init__(self, vocab_file, eos_token="</s>", unk_token="<unk>", additional_special_tokens=[], **kwargs):
         super().__init__(
             eos_token=eos_token,
             unk_token=unk_token,
-            pad_token=pad_token,
             additional_special_tokens=additional_special_tokens,
             **kwargs,
         )

src/transformers/tokenization_reformer_fast.py

@@ -102,7 +102,6 @@ class ReformerTokenizerFast(PreTrainedTokenizerFast):
         tokenizer_file=None,
         eos_token="</s>",
         unk_token="<unk>",
-        pad_token="<pad>",
         additional_special_tokens=[],
         **kwargs
     ):
@@ -111,7 +110,6 @@ class ReformerTokenizerFast(PreTrainedTokenizerFast):
             tokenizer_file=tokenizer_file,
             eos_token=eos_token,
             unk_token=unk_token,
-            pad_token=pad_token,
             additional_special_tokens=additional_special_tokens,
             **kwargs,
         )

tests/test_tokenization_reformer.py

@@ -63,6 +63,50 @@ class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         rust_ids = rust_tokenizer.encode(sequence)
         self.assertListEqual(ids, rust_ids)
 
+    def test_padding(self, max_length=15):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                # Simple input
+                s = "This is a simple input"
+                s2 = ["This is a simple input 1", "This is a simple input 2"]
+                p = ("This is a simple input", "This is a pair")
+                p2 = [
+                    ("This is a simple input 1", "This is a simple input 2"),
+                    ("This is a simple pair 1", "This is a simple pair 2"),
+                ]
+
+                # Simple input tests
+                self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
+
+                # Simple input
+                self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
+
+                # Simple input
+                self.assertRaises(
+                    ValueError,
+                    tokenizer_r.batch_encode_plus,
+                    s2,
+                    max_length=max_length,
+                    padding="max_length",
+                )
+
+                # Pair input
+                self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
+
+                # Pair input
+                self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
+
+                # Pair input
+                self.assertRaises(
+                    ValueError,
+                    tokenizer_r.batch_encode_plus,
+                    p2,
+                    max_length=max_length,
+                    padding="max_length",
+                )
+
     def test_full_tokenizer(self):
         tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)